1.7k citations · 1.8k across the 12 of their papers we have counts for
12 papers
Using Joint Models for Longitudinal and Time-to-Event Data to Investigate the Causal Effect of Salvage Therapy after Prostatectomy
Dimitris Rizopoulos, Jeremy M. G. Taylor, Grigorios Papageorgiou +1
Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen (PSA) measurements. When PSA levels r…
Optimizing Dynamic Predictions from Joint Models using Super Learning
Dimitris Rizopoulos, Jeremy M. G. Taylor
Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two com…
A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies
Madeline R. Abbott, Walter H. Dempsey, Inbal Nahum-Shani +3
Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on multiple outcomes measured frequently over time that have the potential t…
Surrogacy Validation for Time-to-Event Outcomes with Illness-Death Frailty Models
Emily K. Roberts, Michael R. Elliott, Jeremy M. G. Taylor
A common practice in clinical trials is to evaluate a treatment effect on an intermediate endpoint when the true outcome of interest would be difficult or costly to measure. We con…
Understanding the dynamic impact of COVID-19 through competing risk modeling with bivariate varying coefficients
Wenbo Wu, John D. Kalbfleisch, Jeremy M. G. Taylor +2
The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. Motivated…
A synthetic data integration framework to leverage external summary-level information from heterogeneous populations
Tian Gu, Jeremy M. G. Taylor, Bhramar Mukherjee
There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External informat…
Kullback-Leibler-Based Discrete Failure Time Models for Integration of Published Prediction Models with New Time-To-Event Dataset
Di Wang, Wen Ye, Randall Sung +4
Prediction of time-to-event data often suffers from rare event rates, small sample sizes, high dimensionality and low signal-to-noise ratios. Incorporating published prediction mod…
A meta-inference framework to integrate multiple external models into a current study
Tian Gu, Jeremy M. G. Taylor, Bhramar Mukherjee
It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of rel…
Inference after black box selection
Jelena Markovic, Jonathan Taylor, Jeremy Taylor
We consider the problem of inference for parameters selected to report only after some algorithm, the canonical example being inference for model parameters after a model selection…
Vertical modeling: analysis of competing risks data with a cure proportion
M. A. Nicolaie, J. M. G. Taylor, C. Legrand
In this paper, we extend the vertical modeling approach for the analysis of survival data with competing risks to incorporate a cured fraction in the population, that is, a proport…
Imaging topological edge states in silicon photonics
M. Hafezi, S. Mittal, J. Fan +2
Topological features - global properties not discernible locally - emerge in systems from liquid crystals to magnets to fractional quantum Hall systems. Deeper understanding of the…
Origin Of Current-Induced Forces In An Atomic Gold Wire: A First Principles Study
Mads Brandbyge, Kurt Stokbro, Jeremy Taylor +2
We address the microscopic origin of the current-induced forces by analyzing results of first principles density functional calculations of atomic gold wires connected to two gold…